DBMS > Apache IoTDB vs. CouchDB vs. DataFS vs. SAP HANA

System Properties Comparison Apache IoTDB vs. CouchDB vs. DataFS vs. SAP HANA

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Editorial information provided by DB-Engines

Name

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

In-memory, column based data store. Available as appliance or cloud service

Primary database model

Secondary database models

Spatial DBMS

using the Geocouch extension

Graph DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	1.31
Rank	#159	Overall
	#14	Time Series DBMS

Score	7.46
Rank	#51	Overall
	#7	Document stores

Score	0.02
Rank	#369	Overall
	#18	Object oriented DBMS

Score	41.32
Rank	#23	Overall
	#16	Relational DBMS

Website

iotdb.apache.org

couchdb.apache.org

newdatabase.com

www.sap.com/products/hana.html

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

docs.couchdb.org/en/stable

dev.mobiland.com/Overview.xsp

help.sap.com/hana

Developer

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

Mobiland AG

SAP

Initial release

2018

2005

2018

2010

Current release

1.1.0, April 2023

3.3.3, December 2023

1.1.263, October 2022

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache version 2

commercial

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Erlang

Server operating systems

All OS with a Java VM (>= 1.8)

Android
BSD
Linux
OS X
Solaris
Windows

Windows

Appliance or cloud-service

Data scheme

yes

schema-free

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

yes

Typing

predefined data types such as float or date

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

via views

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

JDBC
Native API

RESTful HTTP/JSON API

.NET Client API
Proprietary client DLL
WinRT client

JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

.Net
C
C#
C++
VB.Net

Server-side scripts

Stored procedures

yes

View functions in JavaScript

SQLScript, R

Triggers

yes

no, except callback-events from server when changes happened

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Sharding

improved architecture with release 2.0

Proprietary Sharding system

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

atomic operations within a single document possible

ACID

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

yes

Access rights for users can be defined per database

Windows-Profile

yes

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